El Nino-Southern Oscillation Events Research Articles

El Niño southern oscillation, weather patterns, and bacillary dysentery in the Yangtze River Basin, China

Abstract Background Increasingly intense weather anomalies associated with interannual climate variability patterns, like El Niño-southern oscillation (ENSO), could exacerbate the occurrence and transmission of infectious diseases. However, research in China remains limited in understanding the impacts and intermediate weather changes of ENSO on bacillary dysentery (BD). This study aimed to reveal the relationship between ENSO, weather conditions, and the incidence of BD, and to identify the potential meteorological pathways moderated by ENSO in the ENSO-BD connections. Methods BD disease data and meteorological data, as well as ENSO index, from 2005 to 2020 were obtained for 95 cities in the Yangtze River Basin. We first established the associations between ENSO events and BD, ENSO and weather, as well as weather and BDs using two-stage statistical models. Then, we applied a causal mediation analysis to identify the specific meteorological changes in the ENSO-BD relationship. Results In the Yangtze River Basin, both El Niño (IRR: 1.06, 95%CI: 1.04 ~ 1.08) and La Niña (IRR: 1.03, 95%CI: 1.02 ~ 1.05) events were found to increase the risk of BD. Variations of ENSO index were associated with changes in local weather conditions. Both the increases in regional temperatures and rainfall were associated with a higher risk of BD. In the casual mediation analyses, we identified that higher temperatures and excessive rainfall associated with La Niña and El Niño events mediated the ENSO’s effect on BD, with mediation proportions of 38.58% and 34.97%, respectively. Conclusions Long-term climate variability, like ENSO, can affect regional weather conditions and lead to an increased risk of BD. We identified the mediating weather patterns in the relationship between ENSO and BD, which could improve targeted health interventions and establish an advanced early warning system in response to the BD epidemic.

A probabilistic forecast for multi-year ENSO using Bayesian convolutional neural network

Abstract A robust El Niño Southern Oscillation (ENSO) prediction is essential for monitoring the global climate, regional monsoons, and weather extremes. Despite dedicated efforts spanning decades, the precise prediction of ENSO events through numerical modeling beyond a couple of seasonal lead times remains a daunting challenge. The advent of deep learning-based approaches marks a transformative era in climate and weather prediction. However, many machine learning-based studies attempting ENSO prediction are confined to singular estimates, lacking adequate quantification of uncertainty in learned parameters and overlooking the crucial need for a nuanced understanding of ENSO prediction confidence. Here, we introduce a deep learning-based Bayesian convolutional neural network model that provides robust probabilistic predictions for ENSO with a lead time of up to 9–10 months across all seasons. The Bayesian layers within the convolutional neural network maintain the capability to predict a distribution of learned parameters. Augmented with bias correction, our model reproduces the amplitude of the Niño 3.4 index with fidelity for lead up to 9–10 months. The inherent capacity for uncertainty modeling enhances the reliability of bayesian neural networks (BNNs), making them particularly valuable in operational services. This research holds substantial socio-economic implications as it enhances our forecasting capabilities and rigorously quantifies forecast uncertainties, providing valuable insights for planning and policymaking.

The history of the El Niño–Southern Oscillation and sea surface salinity during 1376–1500 CE reconstructed by Porites coral δ18O from Huangyan Island, South China Sea

The El Niño–Southern Oscillation (ENSO) is the most influential climatic phenomenon affecting global ecosystems, water use, and agriculture on an interannual scale. However, limited instrumental records make it difficult to fully understand the characteristics of ENSO events. In this study, we used both monthly-resolved Porites coral δ18O records, i.e., living Porites corals of δ18O–HYDL4 (1992–2015) and subfossil Porites corals of δ18O–HYD3 (1376–1500 CE), from Huangyan Island in the South China Sea (SCS), to reconstruct the moderate intensity ENSO and sea surface salinity (SSS) during 1376–1500 CE. The results show that the SSS was higher but the frequency of moderate-to-high intensity ENSO events was lower than the present during 1376–1500 CE. ENSO activity was generally similar to or slightly lower than that of today, but it included several very strong ENSO events in the first substage of 1376–1450 CE, while it was relatively quiet in the second substage of 1451–1500 CE. More moderate-intensity ENSO events may have occurred in the relatively warm climate. The variation in coral δ18O was dominated by multiple factors in this region. SST, ENSO and the Pacific Decadal Oscillation (PDO) may be the dominant factors influencing the changes in coral δ18O at different timescales.

Revisiting the inconsistent influence of El Niño-Southern Oscillation on the equatorial Atlantic

Abstract The impact of El Niño-Southern Oscillation (ENSO) on the equatorial Atlantic Zonal Mode (AZM) is investigated using two atmospheric reanalysis products and 44 models from the Coupled Model Intercomparison Project Phase 6 (CMIP6). While the impact of ENSO on equatorial Atlantic SST is inconsistent, as previously reported, there is a very robust influence of decaying ENSO events in boreal spring on the contemporaneous surface zonal winds over the equatorial Atlantic, with El Niño events associated with anomalous easterlies that cool the equatorial Atlantic. This dynamic impact is counteracted by a thermodynamic impact, in which El Niño events cause changes in surface heat fluxes that lead to warming over the tropical Atlantic. The thermodynamic influence is active throughout the lifecycle of the ENSO event, but the dynamic influence is highly seasonal with a pronounced peak in boreal spring. Thus, a quickly decaying El Niño event will lead to warming, while a slowly decaying event will lead to cooling in the equatorial Atlantic. The ENSO-AZM relation is further complicated by partially independent Atlantic variability, in particular that associated with the South Atlantic subtropical dipole (SASD). Prediction experiments with a simple linear regression model support the role of the SASD as an AZM precursor. Many CMIP6 models show skillful predictions of the June-July-August AZM based on SST indices in the preceding December-January-February, with correlations above 0.5. When predictions are restricted to ENSO years, even more models exceed this threshold, but skill in the reanalyses remains relatively low, possibly due to insufficient training data.

Exploring climate stabilisation at different global warming levels in ACCESS-ESM-1.5

Abstract. Under the Paris Agreement, signatory nations aim to keep global warming well below 2 °C above pre-industrial levels and preferably below 1.5 °C. This implicitly requires achieving net-zero or net-negative greenhouse gas emissions to ensure long-term global temperature stabilisation or reduction. Despite this requirement, there have been few analyses of stabilised climates, and there is a lack of model experiments to address our need for understanding the implications of the Paris Agreement. Here, we describe a new set of experiments using the Australian Community Climate and Earth System Simulator Earth system model (ACCESS-ESM-1.5) that enables the analysis of climate evolution under net-zero emissions, and we present initial results. Seven 1000-year-long simulations were run with global temperatures stabilising at levels in line with the Paris Agreement and at a range of higher global warming levels (GWLs). We provide an overview of the experimental design and use these simulations to demonstrate the consequences of delayed attainment of global net-zero carbon dioxide emissions. We show that there are substantial differences between transient and stabilising climate states and differences in stabilisation between GWLs. As the climate stabilises under net-zero emissions, we identify significant and robust changes in temperature and precipitation patterns including continued Southern Ocean warming and changes in regional precipitation trends. Changes under net-zero emissions differ greatly between regions, including contrasting trajectories of sea ice extent between the Arctic and Antarctic. We also examine the El Niño–Southern Oscillation (ENSO) and find evidence of reduced amplitude and frequency of ENSO events under climate stabilisation relative to projections under transient warming. An analysis at specific GWLs shows that significant regional changes continue for centuries after emission cessation and that these changes are stronger at higher GWLs. Our findings suggest substantial long-term climate changes are possible even under net-zero emission pathways. These simulations are available for use in the community and will hopefully motivate further experiments and analyses based on other Earth system models.

Impact of ENSO on Cloud Distribution and Rainfall Variability in Tangerang Regency

A type of climate variation in the Pacific Ocean known as El Niño Southern Oscillation (ENSO) is defined by an increase in sea surface temperature (SST) in the Central and Eastern equatorial areas, which affects the amount of rainfall that falls and increases. In Indonesia, the rainy season typically persists from October to March, whereas the dry season persists from April to September. Research on the influence of ENSO on rainfall has been carried out in several areas, but has not been carried out in the Tangerang area. This study aims to ascertain how the ENSO phenomena affect rainfall fluctuations and cloud distribution in the Regency of Tangerang. The secondary data, which includes information on rainfall, cloud cover, wind direction, and speed, was acquired from BMKG Meteorology Budiarto. In addition, data from the Australian Bureau of Meteorology (BOM) website's Southern Oscillation Index (SOI) was used. Surface weather analysis and Pearson correlation analysis are the data analysis techniques used. The results presented that based on surface weather study, ENSO events had a consequence on cloud distribution in Tangerang Regency. The clouds lead when the ENSO phenomenon occurs, namely Cumulus and Nimbostratus clouds with a 4-8 octa cloud cover. Meanwhile, the correlation test results show that ENSO influences seasonal and annual rainfall variations in the district of Tangerang. The largest correlation between SOI and annual rainfall occurred in 2015 with a correlation value of r-0.84 (very strong). Meanwhile, the greatest correlation between SOI and seasonal rainfall occurred in July-September with a correlation value of r=0.67 (strong).

Global Marine Heatwaves Under Different Flavors of ENSO

AbstractMarine heatwaves (MHWs) have caused devasting ecological and socioeconomic impacts worldwide. Understanding the connection of regional events to large‐scale climatic drivers is key for enhancing predictability and mitigating MHW impacts. Despite the reported connection between MHWs globally and El Niño–Southern Oscillation (ENSO), establishing statistically significant links between different types of ENSO events and MHWs remains challenging due to the limited duration of observational data. Here, we use 10,000 years of simulations from a Linear Inverse Model (LIM) to address this issue. Our findings reveal distinct connections between MHWs and ENSO, with diverging influences from different flavors of El Niño and La Niña events. In addition, under long‐lasting El Niño conditions, the likelihood of MHWs increases by up to 12‐fold in the Indian and Pacific Oceans. This study highlights the global connections between ENSO diversity and variations in MHW events.

Advanced Peak Phase of ENSO under Global Warming

Abstract El Niño–Southern Oscillation (ENSO) is the leading mode of interannual ocean–atmosphere coupling in the tropical Pacific, greatly influencing the global climate system. Seasonal phase locking, which means that ENSO events usually peak in boreal winter, is a distinctive feature of ENSO. In model future projections, the ENSO sea surface temperature (SST) amplitude in winter shows no significant change with a large intermodel spread. However, whether and how ENSO phase locking will respond to global warming are not fully understood. In this study, using Community Earth System Model Large Ensemble (CESM-LE) projections, we found that the seasonality of ENSO events, especially its peak phase, has advanced under global warming. This phenomenon corresponds to the seasonal difference in the changes in the ENSO SST amplitude with an enhanced (weakened) amplitude from boreal summer to autumn (winter). Mixed layer ocean heat budget analysis revealed that the advanced ENSO seasonality is due to intensified positive meridional advective and thermocline feedback during the ENSO developing phase and intensified negative thermal damping during the ENSO peak phase. Furthermore, the seasonal variation in the mean El Niño–like SST warming in the tropical Pacific favors a weakened zonal advective feedback in boreal autumn–winter and earlier decay of ENSO. The advance of the ENSO peak phase is also found in most CMIP5/6 models that simulate the seasonal phase locking of ENSO well in the present climate. Thus, even though the amplitude response in the winter shows no model consensus, ENSO also significantly changes during different stages under global warming.

Fingerprints of El Niño Southern Oscillation on global and regional oceanic chlorophyll-a timeseries (1997–2022)

With the advancement of present-day ocean color satellites, the spatiotemporal variations of oceanic Chlorophyll-a (Chl-a) concentration, a proxy for phytoplankton population, can be monitored at regional and global scales. Estimating long-term changes in Chl-a concentration, however, is mainly constrained by the limited availability of ocean color data and the significant influence of climate oscillations like the El Niño Southern Oscillation (ENSO). In this study, we investigate the influence of ENSO on regional and global Chl-a timeseries using two ocean color datasets spanning from September 1997 to December 2022. Our analysis found that global Chl-a concentration exhibits a significant increasing trend over the study period. Most of the increasing trends are detected in the Southern Hemisphere (SH) and high-latitudinal regions, including the Southern Ocean. In light-limited regions like the Southern Ocean, enhanced Chl-a concentration shows a strong positive correlation with warming sea surface temperature (SST). Conversely, most oceans in the Northern Hemisphere (NH) exhibit decreasing trends, which are highly correlated with warming SST and ENSO phases. Additionally, we identify distinctive fingerprints of four extreme ENSO events: two extreme El Niño events (1997–1998 and 2015–2016) and two triple La Niña events (1998–2001 and 2020–2022) in these timeseries. Furthermore, empirical orthogonal function (EOF) analysis reveals that the dominant mode of interannual Chl-a variability is associated with an ENSO-like pattern, accounting for about 13 % of the total variability. Intriguingly, this principal mode is highly correlated with the phases of ENSO (R = -0.87) and Pacific Decadal Oscillation (PDO) (R = −0.90) on an interannual scale. This study provides insights into assessing the impacts of ENSO on long-term Chl-a trend estimation.

The 2023/24 El Niño and the Feasibility of Long-Lead ENSO Forecasting

Abstract Through its atmospheric teleconnections, El Niño–Southern Oscillation (ENSO) shifts and disrupts weather and climate patterns far beyond the equatorial Pacific where it occurs often resulting in catastrophic consequences in many countries of the world. It is also the largest source of seasonal and interannual climate predictability. Despite its huge importance, ENSO forecasting is still not performed operationally at longer leads than about 6 months ahead. At the same time, there is mounting scientific evidence that forecasts are possible even more than a year in advance. Early warning of ENSO events could substantially mitigate some of the most damaging impacts, such as floods, droughts, and harvest failure and help avoid famine, migration, and disease outbreaks. Here, we present forecasts from a statistical ENSO model of the next El Niño predicted to occur in the winter of 2023/24, at lead times between 11 and 17 months ahead of an expected peak in December 2023. We use a statistical unobserved dynamic components model (EDCM) based on subsurface ocean temperatures as well as sea surface temperatures and zonal wind stress. EDCM has been previously validated through hindcasts of the major El Niños since 1970 and through real-time forecasts of the 2015/16 and 2018/19 El Niños. Our statistical framework and results indicate that there is potential for doubling the operational predictive lead time of ENSO to at least 12 months, with additional promise for even earlier anticipation of 19 months. Such longer-lead forecasts could be of high value, because decision-making and management in a number of key socioeconomic sectors could be greatly improved.

MORLET’S WAVELET ANALYSIS ON EL NIÑO SOUTHERN OSCILLATION (ENSO) AND THE INDIAN OCEAN DIPOLE (IOD) FOR 84 YEARS: 1940-2023

As is known, the impact caused by El Niño Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD) can reach extreme levels, especially rainfall in Indonesia. So, updating information on events and cycles of these phenomena is essential. Using Sea Surface Temperature (SST) data spanning the previous 84 years (1940–2023) from ERA5, we examined Sea Surface Temperature Anomalies (SSTA), which serve as a predictive tool for ENSO and IOD events. Apart from that, in this research, SSTA variance analysis was also carried out using Wavelet. The analysis results show several Positive IOD-Like events (1943, 1944, 1977, 1996) and Negative IOD-Like (1985, 1992, 2016). Apart from that, the results of this research also show that El Niño in 2002/03 coincided with Negative IOD in 2002. The results of Wavelet analysis show that the SSTA DMI variance experienced increased activity in the periods 1940-1968, 1969-1991, and 1992-2023. The Wavelet analysis also shows that ENSO activity increased in 1970-2000 and decreased again in 2000-2023.

Dynamic of upwelling variability in southern Indonesia region revealed from satellite data: Role of ENSO and IOD

The Southern Indonesian (SI) region is known for its high-intensity coastal upwelling caused by monsoonal wind. Interannual phenomena such as El Niño Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) also influence upwelling activity in this region. This study analyzed the relationship between upwelling intensity (UIsst) and those variables and their impact on oceanographic features such as Sea Surface Temperature (SST) and chlorophyll-a concentration. We used satellite imagery data, including SST from the National Oceanic and Atmospheric Administration (NOAA) and chlorophyll-a from MODIS, to analyze the aforementioned issue. To identify the impact of wind patterns on coastal upwelling, we analyzed using zonal wind stress from ERA-5 Data. Quantification of UIsst is defined as the SST gradient between the coastal and open ocean waters. Linear and partial correlation analysis between UIsst with the Ocean Niño Index (ONI) and Dipole Mode Index (DMI) were conducted to see the influence of ENSO and IOD phenomena. Anomaly analysis was also conducted on SST, chlorophyll-a concentration, zonal windstress and UIsst to see how large the values were during the years of the ENSO and IOD events. Upwelling in the SI region typically occurs during southeast monsoon (SEM) periods, starting earlier in the East side (Nusa Tenggara Islands) and moving towards the West side (South Coast of Java). The correlation analysis (both linear and partial) indicates that the IOD has a stronger influence on UIsst in the SI region compared to ENSO, especially during June to October (SEM periods). This finding is confirmed by anomaly analysis, which reveals significant changes in SST, chlorophyll-a concentration, zonal windstress, and UIsst during ENSO and IOD events. The magnitude of the anomalies is generally stronger during IOD events than those observed under ENSO conditions.

Influence of El Niño southern oscillation on precipitation variability in Northeast Thailand

This study investigates the influence of El Niño Southern Oscillation (ENSO) on monthly precipitation anomaly (PPTA) in Northeast Thailand using 30 years (1993–2022) data obtained from 27 weather stations of the Thai Meteorological Department (TMD). Pearson correlation analysis elucidates the relationship between ENSO indices and PPTA. Results reveal significant correlations between ENSO indices and PPTA, providing insights into their intricate relationship. La Niña events have a stronger relationship with PPTA than El Niño events, with notable correlations observed.•Strong La Niña phases, PPTA tends to increase across Northeast Thailand when Niño 3, 3.4, and 4 are more strongly negative -0.64, -0.50, and -0.65, respectively. These correlations provide valuable insights into the hydrological influence of ENSO in Thailand.•After the ENSO event, there appears to be a 4–5 month lag period from the impact on PPTA that shows a higher correlation.•Wavelet transform coherence (WTC) analysis dissects the temporal and frequency-specific nature of the relationship between ENSO and PPTA found during the frequency of 2–7 years. These findings underscore the importance of studying regional variations in ENSO impacts for PPTA in Northeast Thailand.

El Niño and positive Indian Ocean Dipole conditions simultaneously reduce the production of multiple cereals across India

Abstract Natural climate phenomena like El Niño Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD) influence the Indian monsoon and thereby the region’s agricultural systems. Understanding their influence can provide seasonal predictability of agricultural production metrics to inform decision-making and mitigate potential food security challenges. Here, we analyze the effects of ENSO and IOD on four agricultural production metrics (production, harvested area, irrigated area, and yields) for rice, maize, sorghum, pearl millet, and finger millet across India from 1968 to 2015. El Niños and positive-IODs are associated with simultaneous reductions in the production and yields of multiple crops. Impacts vary considerably by crop and geography. Maize and pearl millet experience large declines in both production and yields when compared to other grains in districts located in the northwest and southern peninsular regions. Associated with warmer and drier conditions during El Niño, >70% of all crop districts experience lower production and yields. Impacts of positive-IODs exhibit relatively more spatial variability. La Niña and negative-IODs are associated with simultaneous increases in all production metrics across the crops, particularly benefiting traditional grains. Variations in impacts of ENSO and IOD on different cereals depend on where they are grown and differences in their sensitivity to climate conditions. We compare production metrics for each crop relative to rice in overlapping rainfed districts to isolate the influence of climate conditions. Maize production and yields experience larger reductions relative to rice, while pearl millet production and yields also experience reductions relative to rice during El Niños and positive-IODs. However, sorghum experiences enhanced production and harvested areas, and finger millet experiences enhanced production and yields. These findings suggest that transitioning from maize and rice to these traditional cereals could lower interannual production variability associated with natural climate variations.

Similar teleconnection patterns of ENSO-NAO and ENSO-precipitation in Colombia: linear and non-linear relationships.

The Central-Pacific (CP) and Eastern-Pacific (EP) types of El Niño-Southern Oscillation (ENSO) and their ocean-atmosphere effect cause diverse responses in the hydroclimatological patterns of specific regions. Given the impact of ENSO diversity on the North Atlantic Oscillation (NAO), this study aimed to determine the relationship between the ENSO-NAO teleconnection and the ENSO-influenced precipitation patterns in Colombia during the December-February period. Precipitation data from 1981 to 2023, obtained from the Climate Hazards Group (CHIRPS), were analyzed using nine ENSO and NAO indices spanning from 1951 to 2023. Using Pearson's correlation and mutual information (MI) techniques, nine scenarios were devised, encompassing the CP and EP ENSO events, neutral years, and volcanic eruptions. The results suggest a shift in the direction of the ENSO-NAO relationship when distinguishing between the CP and EP events. Higher linear correlations were observed in the CP ENSO scenarios (r > 0.65) using the MEI and BEST indices, while lower correlations were observed when considering EP events along with the Niño 3 and Niño 1.2 indices. MI show difference in relationships based on the event type and the ENSO index used. Notably, an increase in the non-linear relationship was observed for the EP scenarios with respect to correlation. Both teleconnections followed a similar pattern, exhibiting a more substantial impact during CP ENSO events. This highlights the significance of investigating the impacts of ENSO on hydrometeorological variables in the context of adapting to climate change, while acknowledging the intricate diversity inherent to the ENSO phenomenon.

Impact of the extreme 2015-16 El Niño climate event on forest and savanna tree species of the Amazonia-Cerrado transition

Extreme drought events, driven by the El Niño Southern Oscillation (ENSO), are linked to increased tree mortality and alterations in vegetation structure, dynamics, and floristic composition in tropical forests. Existing analyses, primarily focusing on Africa, Central America, and Amazonia, overlook the floristic impacts on biome transitions. This study evaluates the profound effects of the severe 2015/2016 ENSO event on tree density and floristic composition in the critical transition zone between Amazonia and Cerrado, South America's largest biomes. Our findings not only document significant biodiversity loss but also offer insights into species resilience, guiding conservation strategies under changing climate conditions. We inventoried long-term plots before and after the extreme drought event, sampling 12,465 individuals from 526 species, 224 genera, and 65 families, in Open Ombrophilous Forest (OF), Seasonal Forest (SF), Cerradão (CD), and Typical Cerrado (TC). We document the disappearance from our plots of 97 species after the ENSO, with only 61 new species being recorded. The total loss of individuals across the transition zone was almost 10 %. The SF and CD forest plots showed the greatest replacements, species losses, and reductions in tree density. Their markedly seasonal baseline climate probably drove these changes. In most phytophysiognomies, there was an increase in pioneer species and drier environment habitat specialist species, indicating that although many species are vulnerable to extreme climate events, others benefit, especially those with a short life cycle. We found that the vegetation of the Amazonia-Cerrado transition overall is vulnerable to climate anomalies, with widespread loss of tree density and change in floristic composition. Our study also provides a species-by-species list of the most vulnerable and resistant trees which helps point to overall climate change vulnerabilities and assist with initiatives to recover degraded areas.

Modulation of Pacific decadal oscillation on the relationship between El Niño–Southern Oscillation and rainy season onset over the Indo‐China Peninsula

AbstractMonsoon precipitation variability over the Indo‐China Peninsula (ICP) has become more complicated affected by global warming. In this study, the modulation of Pacific decadal oscillation (PDO) on the relationship between El Niño–Southern Oscillation (ENSO) and the rainy season onset over the ICP were investigated. The results showed that the ICP rainy season onset were predominantly correlated with winter sea surface temperature anomalies (SSTAs) in the East Pacific Ocean, with late and early onsets following El Niño and La Niña events, respectively. During the warm and cold PDO phase, the correlations tended to be substantially strengthened and weakened, respectively. Further analysis indicates that PDO significantly influenced the effects of ENSO on the ICP rainy season onset by modulating SSTAs and low‐level wind fields. During the El Niño events, abnormal easterlies over the Bay of Bengal (BoB) and southern ICP suppressed water vapour transporting to the ICP, which may be related to the zonal SST anomaly gradient between the Indian Ocean and the Northwest Pacific Ocean. When the El Niño occurred during the warm PDO phase, the rainy season onsets were later. The anomalous easterlies became stronger corresponds to the increasing zonal sea surface temperature anomaly (SSTA) gradient between the Indian Ocean and the Northwest Pacific Ocean. There was no significant anomaly on the rainy season onset during the cold PDO phase. During the La Niña events, the abnormal westerlies in BOB accelerated water vapour transport, and the rainy season onset were earlier during the warm and cold PDO phase. The modulating effects of PDO on La Niña were less than those on El Niño. These results suggest that the predictability of rainy season onset over the ICP can be improved through PDO and thus help agricultural planning and water resources management.

Mapping the main harmful algal species in the East China Sea (Yangtze River estuary) and their possible response to the main ecological status and global climate change via a global vision

The Yangtze River Estuary (YRE) is one of the areas in China most severely affected by harmful algal blooms (HABs). This study explored the distributive patterns of HABs in the YRE and how they are influenced by the El Niño-Southern Oscillation (ENSO) and other environmental factors. Quantitative real-time PCR (qPCR) was employed to detect and quantify the four predominant HAB species in the YRE, Karenia mikimotoi, Margalefidinium polykrikoides, Prorocentrum donghaiense, and Heterosigma akashiwo. Additionally, the study analyzed how turbidity, pH, salinity, and temperature influence these algae. Distribution of the four HAB species in the YRE area shows clear geographical variations: K. mikimotoi is predominantly found in the northwest and central sea areas, M. polykrikoides (East Asian Ribotype, EAR) is mainly distributed in the southeastern part, P. donghaiense is abundant in the northern regions, and H. akashiwo is especially prevalent at stations S26 and S27 in the northeastern part of the study area. HABs dominated by H. akashiwo and P. donghaiense were observed in the northeastern sea area of the YRE on July 22, 2020. Our study reveals that K. mikimotoi, M. polykrikoides (EAR), and P. donghaiense are mainly affected by turbidity, pH, and salinity, while temperature predominantly influences the blooms of H. akashiwo. Moreover, runoff in the YRE has a certain correlation with ENSO events, which may also impact the nutrient content of the region. The findings of this study illustrate the distributive patterns of the four HAB species under various ecological conditions in the YRE and emphasize the importance of establishing practical cases for future warning systems. To better understand how climate change affects HABs, exploring the link between ENSO and HABs is essential.

Improving the Predictability of the US Seasonal Surface Temperature With Convolutional Neural Networks Trained on CESM2 LENS

AbstractTo better understand and improve the prediction of the seasonal surface temperature (TS) across the United States, we employed convolutional neural network (CNN) models trained on the Community Earth System Model Version 2 Large Ensemble (CESM2 LENS). We used lagged sea surface temperatures (SST) over the tropical Pacific region, containing the information of the El Niño Southern Oscillation (ENSO), as input for the CNN models. ENSO is the principal driver of variability in seasonal US surface temperatures (TSUS) and employing CNN models allows for spatiotemporal aspects of ENSO to be analyzed to make seasonal TSUS predictions. For predicting TSUS, the CNN models exhibited significantly improved skill over standard statistical multilinear regression (MLR) models and dynamical forecasts across most regions in the US, for lead times ranging from 1 to 6 months. Furthermore, we employed the CNN models to predict seasonal TSUS during extreme ENSO events. For these events, the CNN models outperformed the MLR models in predicting the effects on seasonal TSUS, suggesting that the CNN models are able to capture the ENSO‐TSUS teleconnection more effectively. Results from a heatmap analysis demonstrate that the CNN models utilize spatial features of ENSO rather than solely the magnitude of the ENSO events, indicating that the improved skill of seasonal TSUS is due to analyzing spatial variation in ENSO events. The proposed CNN model demonstrates a promising improvement in prediction skill compared to existing methods, suggesting a potential path forward for enhancing TSUS forecast skill from subseasonal to seasonal timescales.

Temporal and Spatial Variations in Rainfall Erosivity on Hainan Island and the Influence of the El Niño/Southern Oscillation

Rainfall erosivity (RE), a pivotal external force driving soil erosion, is impacted by El Niño/Southern Oscillation (ENSO). Studying the spatiotemporal variations in RE and their response to ENSO is essential for regional ecological security. In this study, a daily RE model was identified as a calculation model through an evaluation of model suitability. Daily precipitation data from 1971 to 2020 from 38 meteorological stations on Hainan Island, China, were utilized to calculate the RE. The multivariate ENSO index (MEI), Southern Oscillation Index (SOI), and Oceanic Niño Index (ONI) were used as the ENSO characterization indices, and the effects of ENSO on RE were investigated via cross-wavelet analysis and binary and multivariate wavelet coherence analysis. During the whole study period, the average RE of Hainan Island was 15,671.28 MJ·mm·ha−1·h−1, with a fluctuating overall upward trend. There were spatial and temporal distribution differences in RE, with temporal concentrations in summer (June–August) and a spatial pattern of decreasing from east to west. During ENSO events, the RE was greater during the El Niño period than during the La Niña period. For the ENSO characterization indices, the MEI, SOI, and ONI showed significant correlations and resonance effects with RE, but there were differences in the time of occurrence, direction of action, and intensity. In addition, the MEI and MEI–ONI affected RE individually or jointly at different time scales. This study contributes to a deeper understanding of the influence of ENSO on RE and can provide important insights for the prediction of soil erosion and the development of related coping strategies.